Google llc (20240119307). Personalized Federated Learning Via Sharable Basis Models simplified abstract

Personalized Federated Learning Via Sharable Basis Models

Organization Name

google llc

Inventor(s)

Hong-You Chen of Hilliard OH (US)

Boqing Gong of Bellevue WA (US)

Mingda Zhang of Pittsburgh PA (US)

Hang Qi of Mountain View CA (US)

Xuhui Jia of Seattle WA (US)

Li Zhang of Seattle WA (US)

Personalized Federated Learning Via Sharable Basis Models - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119307 titled 'Personalized Federated Learning Via Sharable Basis Models

Simplified Explanation

The embodiments of this patent application focus on personalized federated learning models created through shareable federated basis models. A model architecture and learning algorithm for these personalized models are described, where a set of basis models are learned and combined layer by layer to form a unique model for each client using specific combination coefficients. The basis models are shared among a group of clients, but each client can generate a personalized model based on their individual combination coefficients encoded in a personalized vector.

• Personalized federated learning models created through shareable federated basis models
• Model architecture and learning algorithm for personalized models
• Set of basis models learned and combined for each client using specific combination coefficients
• Basis models shared among clients, allowing for personalized models
• Individual combination coefficients encoded in personalized vectors for each client

Potential Applications

The technology described in this patent application could be applied in various fields such as healthcare, finance, marketing, and more where personalized machine learning models are needed for different clients or users.

Problems Solved

This technology solves the problem of creating personalized machine learning models for individual clients while still utilizing a shared set of basis models, reducing the need for extensive individual model training.

Benefits

The benefits of this technology include improved model personalization, reduced computational resources required for training individual models, and enhanced privacy as the basis models are shared among clients without sharing sensitive data.

Potential Commercial Applications

Potential commercial applications of this technology include personalized recommendation systems, targeted advertising, personalized healthcare diagnostics, and more, where individualized machine learning models are essential for providing tailored services to clients.

Possible Prior Art

One possible prior art in this field is the use of transfer learning techniques to personalize machine learning models for different clients or users. Transfer learning involves adapting a pre-trained model to a new task or domain, which shares similarities with the concept of using shared basis models in this patent application.

How does this technology handle data privacy and security concerns for individual clients?

The patent application does not provide detailed information on how data privacy and security concerns are addressed when sharing basis models among clients. It would be important to understand the mechanisms in place to ensure client data protection.

What are the potential limitations or challenges in implementing this technology in real-world applications?

The patent application does not discuss potential limitations or challenges that may arise when implementing this technology in practical settings. Understanding these factors is crucial for assessing the feasibility and scalability of the proposed approach.

Original Abstract Submitted

the embodiments are directed towards providing personalized federated learning (pfl) models via sharable federated basis models. a model architecture and learning algorithm for pfl models is disclosed. the embodiments learn a set of basis models, which can be combined layer by layer to form a personalized model for each client using specifically learned combination coefficients. the set of basis models are shared with each client of a set of the clients. thus, the set of basis models is common to each client of the set of clients. however, each client may generate a unique pfl based on their specifically learned combination coefficients. the unique combination of coefficients for each client may be encoded in a separate personalized vector for each of the clients.